Triple
T34822696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Ballack |
E1003828
|
entity |
| Predicate | clubHonoursWithFCBayernMunich |
P181522
|
FINISHED |
| Object | Bundesliga titles |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Bundesliga titles | Statement: [Michael Ballack, clubHonoursWithFCBayernMunich, Bundesliga titles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clubHonoursWithFCBayernMunich Context triple: [Michael Ballack, clubHonoursWithFCBayernMunich, Bundesliga titles]
-
A.
BayernMunichEuropeanCupFinalAppearanceNumber
Indicates the number of times Bayern Munich has appeared in a European Cup final.
-
B.
clubNumberOfEuropeanCupsWonWithBayern
Indicates the number of European Cups/Champions League titles a club has won specifically while playing as Bayern Munich.
-
C.
goalsForBayernMunich
Indicates the number of goals that were scored in favor of Bayern Munich in a given match or context.
-
D.
bayernTitleCount
Indicates the number of titles that Bayern Munich has won.
-
E.
clubNumberAtBayernMunich
Indicates the jersey number a player wears or wore while playing for Bayern Munich.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76db717088190811b4e744610f37d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77adeac048190bbe22f1b663009d5 |
completed | May 3, 2026, 4:42 p.m. |
| PD | Predicate disambiguation | batch_69f7795b1abc8190823664d1caa94649 |
completed | May 3, 2026, 4:35 p.m. |
| PDg | Predicate description generation | batch_69f77a39135081908ae22d2a23b44e74 |
completed | May 3, 2026, 4:39 p.m. |
Created at: May 3, 2026, 4 p.m.